This project develops a logistic regression model to identify key predictors of heart disease. After cleaning and recoding variables, three variable selection methods — stepwise, forward, and backward — are applied to optimize model simplicity and predictive power. A confusion matrix evaluates accuracy, sensitivity, specificity, and precision. Inspired by a DataCamp project.